excel-skills

Plugin excel-skills from marketplace vector-agentic-skills in MuhammadHusnainAli/vector-agentic-skills. Commands read from the repo's manifest — 18 days ago.

Skills (159)

• anomaly-detection-model-builder

  Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, P

• catboost-model-builder

  Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detect

• categorical-encoding-selector

  Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encod

• classification-model-selector

  Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, expla

• clustering-algorithm-selector

  Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with de

• collaborative-filtering-implementer

  Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise

• counterfactual-explanation-generator

  Generate counterfactual explanations and algorithmic recourse: the smallest realistic change to a row's features that changes the model's decision. Solves exact minimal-cost recourse for linear and logistic models (MAD-scaled L1 cost, immutable features, increase/decrease directions, bounds, integer

• cross-validation-strategy-designer

  Design the cross-validation scheme that mirrors how a model will be used, and check the folds before trusting any score: StratifiedKFold and repeated CV for i.i.d. data, GroupKFold / StratifiedGroupKFold / LeaveOneGroupOut when entities repeat (customers, patients, devices, sessions), TimeSeriesSpli

• dbscan-parameter-tuner

  Tune DBSCAN (and know when to switch to HDBSCAN or OPTICS): choose min_samples from dimensionality and the smallest meaningful group, find eps from the knee of the k-distance curve with a noise-share bracket, grid-search eps x min_samples for stable regions (cluster count that persists across neighb

• feature-engineering-idea-generator

  Generate concrete, leakage-aware feature ideas for tabular ML and the pandas code to build them: infer column roles (IDs, entity keys, datetimes incl. unix epochs, years, amounts, counts, proportions, low- and high-cardinality categoricals, codes like zip, lat/lon, free text), then propose date part

• feature-importance-reporter

  Produce honest feature importance reports by computing several methods on held-out data and explaining where they disagree: impurity/gain importance (LightGBM, XGBoost, CatBoost, sklearn MDI), standardised coefficients, permutation importance with repeats and standard deviations, grouped permutation

• feature-scaling-advisor

  Decide whether and how to scale or transform numeric features for a given model: no scaling for trees and gradient boosting, StandardScaler, RobustScaler for outliers, MinMaxScaler and MaxAbsScaler (sparse data), log1p, Box-Cox and Yeo-Johnson PowerTransformer for skew, QuantileTransformer for heavy

• feature-selection-analyzer

  Select features for tabular ML without fooling yourself: filter methods (missingness, near-zero variance, ID-like columns, univariate out-of-fold AUC / Spearman / mutual information, correlation-based redundancy groups keeping the strongest member), embedded methods (L1/elastic net, tree and GBM imp

• feature-store-schema-designer

  Design feature store schemas that serve the same features to training and production: entities and join keys, sources with event and created timestamps, feature views grouped by entity and refresh cadence, dtypes, TTLs matched to source cadence, online vs offline stores, freshness SLAs, point-in-tim

• fraud-detection-model-designer

  Design fraud detection models that survive production: point-in-time velocity and behaviour features per card, account or device (counts and amounts over sliding windows that exclude the current event, time since previous, amount relative to history, first-time merchant category or country), out-of-

• hyperparameter-search-planner

  Plan hyperparameter searches that fit the compute budget and read their results correctly: choose grid, random, successive halving (HalvingRandomSearchCV), Bayesian/TPE (Optuna) or Hyperband/ASHA from fit cost, trial budget, parallel workers and the number of continuous parameters; define search spa

• imbalanced-class-handler

  Handle imbalanced classification (fraud, churn, defaults, failures, rare diseases) without fooling yourself: measure with PR-AUC, recall at capacity and expected cost instead of accuracy, keep test sets at natural prevalence, stratified and repeated CV for few positives, choose thresholds from capac

• kmeans-cluster-count-finder

  Choose the number of clusters k for k-means (and k-means-like methods) with evidence instead of a single elbow plot: inertia elbow via the kneedle method, silhouette, Calinski-Harabasz, Davies-Bouldin, the gap statistic against uniform reference data (which also detects that there is no structure at

• learning-curve-diagnoser

  Build and read learning curves (training and validation score versus training-set size) to decide whether a model is data-limited, variance-limited or bias-limited, and therefore whether to collect more data, regularise, add capacity or engineer features. Generates scikit-learn learning_curve script

• learning-to-rank-model-builder

  Build learning-to-rank models for search, recommendations re-ranking and marketplace sorting: validate query-document data (contiguous query groups, single-label and no-relevant queries, duplicates, query-level features, single-feature baselines and leakage-like features), train LightGBM lambdarank

• lime-explanation-generator

  Generate LIME (Local Interpretable Model-agnostic Explanations) for tabular predictions that are reproducible and honest: run LimeTabularExplainer with training statistics, categorical encoding decoded inside the prediction function (so Pipelines with one-hot encoders work), quartile rules or contin

• ml-baseline-model-builder

  Build the baselines every ML model must beat before anyone trusts it: dummy majority/prior/mean/median predictors, the current business rule, a one-feature model (OneR), seasonal-naive and last-value forecasts, popularity recommenders, and a first honest linear model and HistGradientBoosting inside

• ml-feasibility-assessor

  Decide GO, GO WITH RISKS or NO-GO for an ML project before building it: enough labelled rows and minority examples, events per feature, label quality and coverage, history length and drift, single-feature signal (out-of-fold AUC / Spearman) and suspicious near-perfect predictors, data availability a

• ml-model-comparison-reporter

  Compare machine learning models honestly and write the comparison report: per-fold CV scores analysed with the corrected resampled t-test (Nadeau-Bengio), Wilcoxon signed-rank, Bayesian correlated t-test with a region of practical equivalence, and the Friedman test with average ranks for many models

• ml-problem-framing-advisor

  Turn a vague business ask (\"use AI to reduce churn\", \"predict demand\", \"find fraud\") into a precise, buildable ML problem: the decision and action the prediction drives, who acts and with what capacity, the unit and moment of prediction, the label with its observation and outcome windows, labe

• ml-project-scoping-writer

  Write the scoping document (one-pager, PRD section or project charter) for a machine learning project: summary, the decision and its users, success criteria with a primary metric at the operating point, target, baseline, guardrails and online confirmation, data sources with availability at predictio

• ml-success-metric-selector

  Choose the metric that defines success for an ML model, and the guardrails around it, from the decision it serves: precision/recall@K for capacity-bound actions, expected cost at the cost-optimal threshold for asymmetric errors, PR-AUC vs ROC-AUC for rare events, log loss/Brier/ECE when probabilitie

• ml-vs-rules-decision-advisor

  Decide whether a problem should be solved with hand-written rules, machine learning, a hybrid of both, or not yet: score twelve factors (labelled data volume, label availability, signal complexity, change rate, rule maintenance pain, scale, error cost, explainability and regulation, latency, team ML

• model-card-writer

  Write model cards that hold up to review: model details, intended and out-of-scope uses, training and evaluation data with periods and label definitions, overall metrics with confidence intervals, the baseline and decision threshold, performance disaggregated by group or segment with bootstrap inter

• optuna-study-writer

  Write correct, resumable Optuna hyperparameter studies for scikit-learn, LightGBM, XGBoost and CatBoost: an objective with a search space sized to the data, fixed CV folds built once (stratified, grouped or time-ordered), early stopping inside each fold, trial.report and should_prune for MedianPrune

• overfitting-underfitting-diagnoser

  Diagnose overfitting and underfitting from the evidence a model leaves behind: training histories (metric per boosting round or epoch from LightGBM, XGBoost, CatBoost, Keras or PyTorch) and validation curves (train and validation score across one hyperparameter such as max_depth, C, min_samples_leaf

• partial-dependence-plot-builder

  Build and read partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) for tabular models: generate scikit-learn partial_dependence scripts with percentile grids, the right response method and class, integer-safe columns, centred ICE with t

• pca-dimensionality-reducer

  Apply PCA correctly for dimensionality reduction, visualisation, decorrelation, compression and monitoring: standardise first (or justify covariance PCA), choose the number of components with cumulative explained variance, scree/elbow, Kaiser's rule and parallel analysis, interpret components throug

• recommendation-system-designer

  Design recommendation systems from the interaction data up: profile users, items, density, cold start, popularity concentration (Gini, long-tail shares), explicit versus implicit feedback and repeats; choose between popularity, item-item kNN, matrix factorisation (ALS, BPR, EASE), content-based, hyb

• regression-model-trainer

  Train regression models whose loss matches the target: squared error, absolute/Huber for heavy tails, log1p targets with retransformation (smearing) correction, Gamma for positive skewed amounts, Poisson for counts with exposure offsets, Tweedie for zero-inflated losses, two-part hurdle models, logi

• scikit-learn-pipeline-builder

  Build correct, production-shaped scikit-learn pipelines: Pipeline and ColumnTransformer per column role (impute + scale, one-hot with infrequent grouping, cross-fitted TargetEncoder, native categoricals for HistGradientBoosting, TF-IDF + SVD for text, date transformers), the right CV splitter (Strat

• shap-value-explainer

  Compute and explain SHAP values for tabular models correctly: choose the explainer (TreeExplainer interventional or tree_path_dependent, CatBoost native ShapValues, LinearExplainer, model-agnostic PermutationExplainer), pick the background data and the output space (log-odds versus probability), exp

• survival-analysis-model-builder

  Build time-to-event (survival) models that handle censoring correctly: Kaplan-Meier curves with Greenwood log-log confidence intervals, median survival with CIs, survival at chosen times, restricted mean survival time and the log-rank test; Cox proportional hazards with Efron ties, hazard ratios, co

• target-leakage-detector

  Find and fix target leakage, the reason models score 0.99 offline and fail in production: features written after the prediction time (status, cancellation reason, refunds, chargebacks, closed dates), missingness patterns filled by the outcome, future timestamps, aggregates over all history, identifi

• time-based-feature-builder

  Build leakage-safe time-based features from event logs: point-in-time window aggregates (counts, sums, means, max, distinct counts over 1h/24h/7d/30d) strictly before each cutoff, recency (time since last event), frequency and monetary (RFM), velocity and burst ratios, trends (short vs long window),

• time-series-forecasting-model-builder

  Build forecasting models that beat honest baselines: profile the series (frequency, gaps, duplicate timestamps, seasonality by autocorrelation, trend and seasonal strength, weekday and month effects, outliers, smooth/erratic/intermittent/lumpy demand by ADI and CV^2), backtest seasonal naive, drift,

• train-test-split-auditor

  Audit a train/test (or train/validation, train/production) split before trusting test scores: exact duplicate rows across sets, entity leakage (the same customer, patient, device or session on both sides), time overlap in forward splits, near-duplicates (rounded numerics, case/whitespace variants, e

• umap-tsne-embedding-visualizer

  Make and read 2-D maps of high-dimensional data (tabular features, embeddings from text/image models, single-cell style data) with UMAP, t-SNE and PCA without being misled: scaling or L2 normalisation, PCA pre-reduction to ~50 dimensions, UMAP n_neighbors/min_dist/metric and t-SNE perplexity/early e

• uplift-model-builder

  Build uplift (incremental response, heterogeneous treatment effect) models that target the people a campaign, offer or intervention actually changes: validate the randomised experiment (sample-ratio mismatch, covariate balance by standardised mean difference, average treatment effect with confidence

• xgboost-lightgbm-tuner

  Tune XGBoost and LightGBM models efficiently and read their training curves: validation design and early stopping inside CV, fixed learning rate for search then lower rate with more trees for the final fit, capacity knobs (num_leaves, max_depth, min_child_samples, min_child_weight) sized to the data

• academic-english-eap-course-planner

  Plan English for Academic Purposes (EAP) courses: pre-sessional, in-sessional, foundation, IELTS preparation and discipline-specific EAP, with syllabus strands (academic reading, lecture listening and note-taking, essay and report writing, paraphrase, summary and synthesis, referencing and plagiaris

• actfl-proficiency-curriculum-mapper

  Map world language curricula to the ACTFL framework: proficiency levels (Novice, Intermediate, Advanced, Superior, Distinguished with sublevels), functions, contexts, text types and accuracy, proficiency vs performance, the three modes of communication (interpretive, interpersonal, presentational),

• adult-literacy-esl-lesson-builder

  Build lessons for adult ESL learners who are developing literacy (LESLLA learners: pre-literate, non-literate, semi-literate, literate in a non-Roman script): learner profiles and sensitive initial assessment, principles (oral language first, known to new, systematic phonics with adult-relevant text

• authentic-material-lesson-adapter

  Turn authentic material (news articles, podcasts, videos, songs, menus, timetables, adverts, social media posts, forms, infographics) into language lessons by grading the task rather than the text. Selects texts for interest, relevance, exploitability, coverage, cultural load and rights; finds the 4

• can-do-statement-writer

  Write and review can-do statements for language courses, units, lessons and learner self-assessment, CEFR or NCSSFL-ACTFL style: action (observable verb) + text or situation + level-specific conditions, learner ('I can') and descriptor ('Can') versions, progressions of one activity from A1 to C1, an

• cefr-aligned-syllabus-builder

  Build language course syllabuses aligned to the CEFR (A1-C2, plus levels, Companion Volume 2020): entry and exit levels, guided learning hours and course length (with an L1-distance adjustment), syllabus type (topic, functional-notional, task-based, skills-based, multi-strand), unit-by-unit can-do o

• classroom-management-for-esl-coach

  Coach ESL/EFL teachers in classroom management: the instruction sequence (attention, group first, graded imperatives, demonstration, ICQs, hand out, start, check), ICQ examples, online instructions, routines for the first weeks (class agreement, L1 use, phones, lateness, homework, seating), strategi

• communicative-activity-generator

  Generate communicative language-class activities matched to level, skill, class size, time, preparation, online or face-to-face delivery and target language: information gaps, find someone who, role plays with goal-and-conflict role cards, jigsaw reading, ranking and pyramid discussions, dictogloss,

• delta-assignment-planner

  Plan and check Cambridge Delta assignments: the three modules (exam, Module 2 professional practice with PDA and LSAs, Module 3 specialism or management), LSA components (background essay, lesson plan and commentary, observed lesson, post-lesson evaluation), a Module 3 structure, a five-week LSA tim

• dialogue-for-classroom-writer

  Write natural model dialogues for language classes that contain the target language, suit the level and sound spoken rather than written: situation, speakers' goals and a small complication, contractions, reactions and backchannels, discourse markers, repair and softening, adjacency pairs, register,

• esl-worksheet-generator

  Create ESL/EFL and other language worksheets that move from recognition to controlled to freer practice: gap-fills with shuffled word banks, distractors and first-letter hints, open cloze, matching (word-definition, sentence halves, question-answer, collocations) with no item left opposite its partn

• esp-english-course-designer

  Design English for Specific Purposes (ESP) courses for professions and workplaces (medical and nursing, aviation, legal, hospitality, engineering, business): needs analysis (target situation, present situation, learning needs, means analysis) with practitioner interviews, task-list surveys rating fr

• formative-assessment-for-language-planner

  Plan formative assessment for language lessons and units: Wiliam's five strategies, success criteria, a bank of quick checks (exit tickets, mini whiteboards, ABCD cards, hinge questions with misconception distractors, traffic lights, no-hands-up questioning, monitoring checklists, 3-2-1, self-assess

• l2-error-correction-strategy-advisor

  Advise language teachers on correcting learner errors: mistakes vs errors vs fossilised errors, what to correct first (global, target, frequent, offensive), immediate vs delayed correction by lesson stage, the six oral corrective feedback types (explicit correction, recast, clarification request, me

• language-course-scope-sequence-planner

  Plan the scope and sequence of a language course: what is covered and in which order, using sequencing principles (need first, frequency, simple to complex, prerequisites, contrast after both items, learnability, topic coherence, spiral), a recycling plan for every item, a scope and sequence chart,

• language-lesson-differentiation-planner

  Plan differentiation for mixed-level language classes: tiered tasks (must/should/could, same text with different questions, graded texts, choice boards, open-ended tasks), different outcomes, scaffolds that do not stigmatise, same-level and mixed-level grouping by stage, roles, time and fast-finishe

• language-lesson-plan-builder

  Build and review staged language lesson plans for ESL/EFL and other languages: class profile, a main aim written as a learner outcome, subsidiary aims, target language analysis, stages with stage aims, timings, interaction patterns and procedure, materials, anticipated problems with solutions and a

• language-placement-test-builder

  Build, analyse and calibrate language placement tests: a blueprint (levels to separate, sections, length, items per level, item types, delivery, parallel forms), level-targeted grammar, vocabulary, reading and listening items, a short speaking interview with probes and a level-up push, cut-score set

• language-program-review-writer

  Write evidence-based reviews of language programmes and schools: review areas used by quality schemes (aims and design, curriculum and materials, teaching and learning, assessment, learner outcomes, support and welfare, staffing and CPD, resources, management, published information), evidence source

• language-quiz-generator

  Generate language quizzes for grammar, vocabulary and comprehension from a tagged question bank: multiple choice with distractors drawn from real learner errors, gap-fills with base forms and all acceptable answers, true/false, balanced sampling across topics, shuffled questions and options by seed

• language-teacher-cv-writer

  Write and review CVs, cover letters and interview preparation for language teachers (EFL/ESL/TESOL and other languages): one-to-two-page structure, a profile line, qualifications first (CELTA, CertTESOL, Delta, PGCE, MA TESOL), experience bullets with action verbs, levels, ages, scale and results, p

• language-teacher-observation-form-builder

  Build lesson observation forms for language teachers and track observation results: a criteria bank across planning and aims, classroom management, language clarification, skills development, learner engagement, error correction, assessment for learning, inclusion and differentiation, materials and

• listening-task-builder

  Build listening lessons and tasks from audio or video transcripts: checks speech rate (words per minute), length, number of speakers and plays against the CEFR level, suggests pause points with approximate timestamps, and stages the lesson (lead-in, pre-teach, gist, specific information and detail t

• online-language-teaching-setup-advisor

  Advise language teachers on setting up and teaching online: equipment priorities (connection and upload speed, headset microphone, headphones, front lighting, camera, backup), platform features that matter for language classes (breakout rooms, computer-audio sharing, whiteboard and annotation, chat,

• ppp-lesson-structure-planner

  Plan presentation-practice-production (PPP) language lessons: a context that makes meaning clear, clarification of meaning, form and pronunciation with concept-checking questions (CCQs), timelines and drills, controlled practice with feedback, personalised freer practice and delayed error correction

• pronunciation-feedback-for-teachers-tool

  Help teachers diagnose and give feedback on English learners' pronunciation with intelligibility as the goal: Lingua Franca Core priorities, likely difficulties by first language (Spanish, Portuguese, French, German, Italian, Arabic, Mandarin, Cantonese, Japanese, Korean, Vietnamese, Thai, Russian,

• reading-comprehension-text-adapter

  Adapt English reading texts to a target CEFR level and write comprehension tasks for them. Estimates text level from average sentence length and coverage by a high-frequency word list (plus Flesch reading ease), lists less frequent words and sentences too long for the target level, and guides simpli

• recast-and-prompt-technique-coach

  Coach language teachers in oral corrective feedback moves: recasts (full, partial, stressed, recast plus prompt, didactic), prompts (elicitation, metalinguistic clues and questions, clarification requests, repetition, gestures) and explicit correction, with teacher language for each, a prompt-to-cor

• refugee-language-support-planner

  Plan language support for refugees, asylum seekers and other new arrivals: intake with interpreters and literacy checks, pathways (foundation literacy, script transition for learners literate in non-Roman scripts, settlement ESOL, work and study), a settlement syllabus prioritising personal informat

• speaking-rubric-builder

  Build and validate speaking assessment rubrics for language learners: holistic vs analytic vs checklist, criteria (fluency and coherence, lexical resource, grammatical range and accuracy, pronunciation and intelligibility, interactive communication, task achievement), band descriptors written from t

• student-progress-report-writer

  Write language learners' progress reports and report-card comments that are specific, balanced and useful to learners and parents: strength with evidence, measured progress, one or two areas to develop and a concrete next step, a phrase bank by skill, plain-language CEFR level descriptions, tone gui

• student-writing-feedback-generator

  Generate written feedback on language learners' writing: content and task first, then two or three language focus areas, strength-target-action comments, level-appropriate wording, a comment bank for task, organisation, language, register and next steps, learner self- and peer-check checklists, and

• task-based-language-lesson-designer

  Design task-based language teaching (TBLT) lessons that pass Ellis's task criteria (focus on meaning, an information, opinion or reasoning gap, learners' own resources, a non-linguistic outcome) and follow the Willis framework: pre-task, task, planning, report and language focus with analysis and pr

• tefl-celta-prep-tutor

  Tutor trainees preparing for CELTA, Trinity CertTESOL and other initial TEFL/TESOL courses: course structure and assessment (teaching practice, assignments, grades, the five CELTA syllabus topics), what trainers assess in teaching practice, a pre-course study plan, grammar and phonology essentials,

• vocabulary-game-generator

  Generate vocabulary games for language classes from a word list: printable word searches (easy, medium and hard directions, solution grid and positions), unique bingo cards with free squares and a shuffled caller list, and 20 classroom games (back to the board, taboo, pelmanism, dominoes, word-forma

• warm-up-icebreaker-generator
• writing-rubric-for-l2-builder
• young-learner-esl-activity-generator
• 8d-problem-solving-report-writer
• acceptance-sampling-plan-selector
• approved-supplier-list-manager
• apqp-phase-planner
• as9100-gap-analyzer
• audit-report-writer
• capa-effectiveness-checker
• capa-plan-writer
• control-plan-writer
• design-of-experiments-planner
• document-control-system-designer
• five-whys-analysis-facilitator
• gage-rr-msa-analyzer
• green-belt-exam-tutor
• hypothesis-test-selector
• iatf-16949-requirement-checker
• incoming-inspection-plan-writer
• internal-audit-schedule-planner
• iso-9001-certification-roadmap-planner
• iso-9001-clause-explainer
• iso-9001-gap-analyzer
• iso-9001-quality-manual-writer
• iso-audit-checklist-generator
• layered-process-audit-designer
• manufacturing-cpk-capability-calculator
• manufacturing-dmaic-charter-writer
• manufacturing-spc-chart-builder
• minitab-analysis-interpreter
• ncr-nonconformance-report-writer
• pareto-defect-analyzer
• ppap-submission-package-builder
• process-fmea-builder
• qms-audit-finding-writer
• qms-procedure-writer
• quality-fishbone-diagram-builder
• quality-policy-objectives-drafter
• six-sigma-dpmo-calculator
• supplier-audit-questionnaire-builder
• supplier-corrective-action-request-writer
• supplier-development-plan-writer
• supplier-quality-scorecard-builder
• western-electric-rules-checker
• excel-advanced-formulas
• excel-formulas
• excel-fundamentals
• python-async-deadlock-debugger
• python-asyncio-pattern-advisor
• python-beginner-concept-tutor
• python-comprehension-refactorer
• python-context-manager-writer
• python-dataclass-converter
• python-exception-handling-designer
• python-gil-bottleneck-analyzer
• python-idiomatic-code-reviewer
• python-multiprocessing-pool-designer
• python-threadpool-executor-builder
• python-trio-anyio-migrator
• python-type-hints-annotator
• rust-borrow-checker-error-explainer
• rust-cargo-workspace-designer
• rust-error-handling-thiserror-anyhow-designer
• rust-lifetime-annotation-advisor
• rust-tokio-async-service-builder
• rust-trait-design-reviewer
• rust-unsafe-block-auditor
• sql-analytics
• sql-data-quality
• sql-debugger
• sql-dialect-translator
• sql-optimizer
• sql-performance-tuning
• sql-query-explainer
• sql-query-writer
• sql-reporting
• sql-syntax-checker
• sql-window-functions

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